Tutorial for spatial neighborhood analysis (SNA) & spatial co-localization score (sCLS) using 10X public data
21 March, 2025
1 Xenium Human Breast Gene Expression
1.1 Preprocessing & Cell annotation
path <- "./../10X_Xenium_sample/Xenium_V1_human_Breast_2fov_outs/"
data_name = stringr::str_split(path, "/")[[1]][length(stringr::str_split(path, "/")[[1]])-1]
dir.create(paste0("./output/figure/",figure_dir,"/",data_name), showWarnings = FALSE)
# Load the Xenium data
data <- ReadXenium(path, outs = c("matrix", "microns"), type = c("centroids", "segmentations"))
## continue the regular LoadXenium
segmentations.data <- list(
centroids = CreateCentroids(data$centroids),
segmentation = CreateSegmentation(data$segmentations))
coords <- CreateFOV(
coords = segmentations.data,
type = c("segmentation", "centroids"),
molecules = data$microns,
assay = "Spatial")
xenium.obj <- CreateSeuratObject(
counts = data$matrix[["Gene Expression"]],
assay = "Spatial")
xenium.obj[["BlankCodeword"]] <- CreateAssayObject(counts = data$matrix[["Unassigned Codeword"]])
xenium.obj[["ControlCodeword"]] <- CreateAssayObject(counts = data$matrix[["Negative Control Codeword"]])
xenium.obj[["ControlProbe"]] <- CreateAssayObject(counts = data$matrix[["Negative Control Probe"]])
xenium.obj[["fov"]] <- coords
rm(data); gc(); gc()## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
## Ncells 15622688 834.4 24461250 1306.4 NA 24461250 1306.4
## Vcells 31214336 238.2 59605184 454.8 49152 59605184 454.8## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
## Ncells 15622858 834.4 24461250 1306.4 NA 24461250 1306.4
## Vcells 31214780 238.2 59605184 454.8 49152 59605184 454.8rm(coords); gc(); gc()## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
## Ncells 15360908 820.4 24461250 1306.4 NA 24461250 1306.4
## Vcells 30483795 232.6 59605184 454.8 49152 59605184 454.8## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
## Ncells 15360914 820.4 24461250 1306.4 NA 24461250 1306.4
## Vcells 30483889 232.6 59605184 454.8 49152 59605184 454.8rm(segmentations.data); gc(); gc()## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
## Ncells 15120794 807.6 24461250 1306.4 NA 24461250 1306.4
## Vcells 29767453 227.2 59605184 454.8 49152 59605184 454.8## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
## Ncells 15120800 807.6 24461250 1306.4 NA 24461250 1306.4
## Vcells 29767547 227.2 59605184 454.8 49152 59605184 454.8xenium.obj <- subset(xenium.obj, subset = nCount_Spatial > 0)
print(dim(xenium.obj@assays$Spatial$counts))## [1] 280 7273xenium.obj@assays$Spatial$counts[1:5,1:5]## 5 x 5 sparse Matrix of class "dgCMatrix"
## aaaiikim-1 aaaljapa-1 aabhbgmg-1 aabpgobe-1 aacemgol-1
## ABCC11 . . . . .
## ACTA2 1 . . . .
## ACTG2 . . . . .
## ADAM9 1 . 2 1 .
## ADGRE5 . . . . .head(xenium.obj@meta.data)## orig.ident nCount_Spatial nFeature_Spatial nCount_BlankCodeword
## aaaiikim-1 SeuratProject 66 32 0
## aaaljapa-1 SeuratProject 95 43 0
## aabhbgmg-1 SeuratProject 215 74 0
## aabpgobe-1 SeuratProject 103 42 0
## aacemgol-1 SeuratProject 95 49 0
## aacnljfi-1 SeuratProject 146 56 0
## nFeature_BlankCodeword nCount_ControlCodeword
## aaaiikim-1 0 0
## aaaljapa-1 0 0
## aabhbgmg-1 0 0
## aabpgobe-1 0 0
## aacemgol-1 0 0
## aacnljfi-1 0 0
## nFeature_ControlCodeword nCount_ControlProbe nFeature_ControlProbe
## aaaiikim-1 0 0 0
## aaaljapa-1 0 0 0
## aabhbgmg-1 0 0 0
## aabpgobe-1 0 0 0
## aacemgol-1 0 0 0
## aacnljfi-1 0 0 0summary(xenium.obj@meta.data$nCount_Spatial)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.0 54.0 109.0 123.3 174.0 673.0summary(xenium.obj@meta.data$nFeature_Spatial)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 31.00 46.00 44.66 59.00 112.00VlnPlot(xenium.obj, features = c("nFeature_Spatial", "nCount_Spatial"), ncol = 2, pt.size = 0)
ImageDimPlot(xenium.obj, fov = "fov", molecules = c("TUBB2B", "PELI1", "CENPF", "KRT23","PDGFRB","ITGAX","KRT14","GZMA"), nmols = 20000)
xenium.obj <- SCTransform(xenium.obj, assay = "Spatial",
conserve.memory = TRUE, vst.flavor="v2")##
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|======================================================================| 100%xenium.obj <- RunPCA(xenium.obj, assay = "SCT", verbose = FALSE)
xenium.obj <- FindNeighbors(xenium.obj,
reduction = "pca",
k.param = 30,
dims = 1:30)
xenium.obj <- RunUMAP(xenium.obj,
reduction = "pca",
n.neighbors = 30L,
min.dist = 0.3,
dims = 1:30)
print("Clustering...")## [1] "Clustering..."# Louvain
snn_pcs <- BuildSNNSeurat(xenium.obj[["pca"]]@cell.embeddings[,1:30],
nn.eps = 0)
resolution_list <- c(0.2, 0.4, 0.6, 0.8, 1.0)
ids_cos <- Reduce(cbind, parallel::mclapply(resolution_list, function(res_use) {
Seurat:::RunModularityClustering(SNN = snn_pcs,
modularity = 1,
resolution = res_use,
algorithm = 3,
n.start = 10,
n.iter = 10, random.seed = 0, print.output = FALSE,
temp.file.location = NULL, edge.file.name = NULL)
}, mc.cores = min(16, length(resolution_list))))
ids_cos %<>% data.frame()
colnames(ids_cos) <- sprintf("res_%.2f", resolution_list)
ids_cos$res_0.20 <- as.character(ids_cos$res_0.20)
ids_cos$res_0.40 <- as.character(ids_cos$res_0.40)
ids_cos$res_0.60 <- as.character(ids_cos$res_0.60)
ids_cos$res_0.80 <- as.character(ids_cos$res_0.80)
ids_cos$res_1.00 <- as.character(ids_cos$res_1.00)
rownames(ids_cos) = rownames(xenium.obj@meta.data)
ids_cos <- ids_cos %>%
dplyr::mutate(across(everything(), ~ factor(.x, levels = sort(unique(.x)))))
xenium.obj <- AddMetaData(xenium.obj, ids_cos)
head(xenium.obj@meta.data)## orig.ident nCount_Spatial nFeature_Spatial nCount_BlankCodeword
## aaaiikim-1 SeuratProject 66 32 0
## aaaljapa-1 SeuratProject 95 43 0
## aabhbgmg-1 SeuratProject 215 74 0
## aabpgobe-1 SeuratProject 103 42 0
## aacemgol-1 SeuratProject 95 49 0
## aacnljfi-1 SeuratProject 146 56 0
## nFeature_BlankCodeword nCount_ControlCodeword
## aaaiikim-1 0 0
## aaaljapa-1 0 0
## aabhbgmg-1 0 0
## aabpgobe-1 0 0
## aacemgol-1 0 0
## aacnljfi-1 0 0
## nFeature_ControlCodeword nCount_ControlProbe nFeature_ControlProbe
## aaaiikim-1 0 0 0
## aaaljapa-1 0 0 0
## aabhbgmg-1 0 0 0
## aabpgobe-1 0 0 0
## aacemgol-1 0 0 0
## aacnljfi-1 0 0 0
## nCount_SCT nFeature_SCT res_0.20 res_0.40 res_0.60 res_0.80 res_1.00
## aaaiikim-1 112 33 0 2 2 2 1
## aaaljapa-1 107 43 0 2 0 2 1
## aabhbgmg-1 139 69 0 3 3 3 1
## aabpgobe-1 111 42 0 0 0 2 1
## aacemgol-1 106 49 0 3 3 3 3
## aacnljfi-1 132 56 0 0 0 2 1resolution = "0.60"
Idents(xenium.obj) = xenium.obj@meta.data[,paste0("res_",resolution)]
cluster_col = paste0("res_",resolution)g = CellDimPlot(
srt = xenium.obj,
group.by = cluster_col,
reduction = "UMAP", theme_use = "theme_blank",
raster = FALSE,
stat_plot_size = 3
)
g 
xenium.obj.markers <- FindAllMarkers(xenium.obj, only.pos = TRUE)
xenium.obj.markers %>%
group_by(cluster) %>%
dplyr::slice_max(avg_log2FC, n = 10) %>%
as.data.frame() ## p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## 1 2.662899e-144 1.1679022 0.672 0.333 7.456118e-142 0 TUBB2B
## 2 9.566100e-58 1.1014546 0.297 0.131 2.678508e-55 0 CAV1
## 3 4.131144e-47 1.0423056 0.288 0.138 1.156720e-44 0 MYBPC1
## 4 1.174734e-09 0.9748890 0.065 0.032 3.289254e-07 0 SERHL2
## 5 5.652455e-83 0.8908141 0.614 0.365 1.582687e-80 0 SLC5A6
## 6 5.093635e-147 0.8779398 0.862 0.585 1.426218e-144 0 CCND1
## 7 4.594085e-54 0.8503573 0.450 0.255 1.286344e-51 0 TRAF4
## 8 6.618904e-26 0.8189392 0.236 0.131 1.853293e-23 0 PTRHD1
## 9 1.403824e-215 0.7650828 0.999 0.890 3.930707e-213 0 AQP1
## 10 7.102044e-03 0.7501358 0.020 0.011 1.000000e+00 0 APOBEC3A
## 11 0.000000e+00 3.0765049 0.955 0.291 0.000000e+00 1 TOP2A
## 12 0.000000e+00 2.7393502 0.719 0.176 0.000000e+00 1 CENPF
## 13 0.000000e+00 2.6929152 0.877 0.266 0.000000e+00 1 MKI67
## 14 1.834973e-179 2.1395594 0.475 0.126 5.137924e-177 1 RTKN2
## 15 6.780687e-134 1.9392679 0.412 0.119 1.898592e-131 1 PCLAF
## 16 3.384295e-47 0.8499877 0.593 0.385 9.476026e-45 1 TUBB2B
## 17 3.258181e-13 0.7497524 0.185 0.108 9.122906e-11 1 FOXC2
## 18 3.059700e-07 0.6872314 0.102 0.060 8.567159e-05 1 TCF7
## 19 6.658867e-33 0.6227176 0.644 0.466 1.864483e-30 1 EIF4EBP1
## 20 9.353109e-12 0.6102920 0.273 0.186 2.618870e-09 1 ANKRD28
## 21 3.338556e-168 3.2232114 0.284 0.042 9.347957e-166 2 KRT23
## 22 1.182827e-14 3.1600350 0.029 0.005 3.311916e-12 2 KRT5
## 23 2.419718e-54 2.4714986 0.144 0.033 6.775211e-52 2 KRT14
## 24 1.462371e-127 2.3292966 0.301 0.064 4.094640e-125 2 KLF5
## 25 5.715703e-55 2.2236511 0.139 0.030 1.600397e-52 2 MLPH
## 26 1.373781e-11 2.2141908 0.037 0.010 3.846588e-09 2 S100A14
## 27 7.226854e-177 1.7475072 0.706 0.299 2.023519e-174 2 KIT
## 28 2.408642e-15 1.7219561 0.071 0.025 6.744196e-13 2 MUC6
## 29 2.346541e-20 1.7013104 0.102 0.037 6.570315e-18 2 CLIC6
## 30 6.904015e-144 1.6591053 0.666 0.306 1.933124e-141 2 CXCR4
## 31 1.372517e-09 0.3277354 0.819 0.710 3.843048e-07 3 PELI1
## 32 4.281185e-03 0.1577536 0.180 0.240 1.000000e+00 3 HOOK2
## 33 2.329050e-03 0.1564239 0.209 0.280 6.521341e-01 3 JUP
## 34 4.800788e-04 0.1394539 0.127 0.186 1.344221e-01 3 DNTTIP1
## 35 1.246445e-03 0.1231136 1.000 0.948 3.490046e-01 3 LDHB
## 36 0.000000e+00 2.5600975 1.000 0.510 0.000000e+00 4 SERPINA3
## 37 8.206200e-03 1.5294855 0.010 0.003 1.000000e+00 4 ADH1B
## 38 9.341991e-05 1.0418794 0.033 0.014 2.615758e-02 4 SPIB
## 39 8.069967e-17 0.9086068 0.241 0.132 2.259591e-14 4 DUSP2
## 40 4.189243e-52 0.8097979 0.717 0.464 1.172988e-49 4 EIF4EBP1
## 41 1.619078e-20 0.7109335 0.432 0.279 4.533418e-18 4 WARS
## 42 2.597619e-44 0.6878823 0.658 0.386 7.273332e-42 4 TUBB2B
## 43 3.768419e-29 0.6653245 0.579 0.374 1.055157e-26 4 SQLE
## 44 3.184379e-04 0.6438372 0.089 0.058 8.916261e-02 4 MYO5B
## 45 1.085285e-42 0.6417512 0.821 0.604 3.038797e-40 4 SEC11C
## 46 0.000000e+00 4.6918879 0.504 0.032 0.000000e+00 5 LRRC15
## 47 0.000000e+00 4.5836602 0.468 0.029 0.000000e+00 5 PDGFRB
## 48 0.000000e+00 4.5139079 0.919 0.156 0.000000e+00 5 POSTN
## 49 0.000000e+00 4.4042333 0.948 0.184 0.000000e+00 5 LUM
## 50 0.000000e+00 4.3352375 0.814 0.110 0.000000e+00 5 SFRP4
## 51 3.304544e-203 4.2674955 0.252 0.018 9.252723e-201 5 C5orf46
## 52 0.000000e+00 4.1969031 0.790 0.104 0.000000e+00 5 CXCL12
## 53 0.000000e+00 4.1827769 0.692 0.066 0.000000e+00 5 CCDC80
## 54 1.416108e-286 3.9928945 0.407 0.040 3.965103e-284 5 GJB2
## 55 3.081503e-185 3.9617716 0.247 0.020 8.628209e-183 5 IGF1
## 56 0.000000e+00 5.5070742 0.646 0.030 0.000000e+00 6 ITGAX
## 57 2.314770e-296 5.1912226 0.293 0.011 6.481356e-294 6 ITGAM
## 58 0.000000e+00 4.9972590 0.559 0.031 0.000000e+00 6 CD163
## 59 0.000000e+00 4.8843382 0.431 0.019 0.000000e+00 6 C1QA
## 60 0.000000e+00 4.8189677 0.887 0.121 0.000000e+00 6 LYZ
## 61 0.000000e+00 4.6206767 0.521 0.031 0.000000e+00 6 IGSF6
## 62 0.000000e+00 4.6107789 0.913 0.106 0.000000e+00 6 FCER1G
## 63 0.000000e+00 4.5730583 0.530 0.034 0.000000e+00 6 FCGR3A
## 64 0.000000e+00 4.5628294 0.833 0.096 0.000000e+00 6 CD68
## 65 0.000000e+00 4.5618335 0.480 0.028 0.000000e+00 6 C1QC
## 66 3.031048e-133 6.8417162 0.173 0.003 8.486933e-131 7 PRF1
## 67 0.000000e+00 6.2092815 0.835 0.029 0.000000e+00 7 CD3E
## 68 0.000000e+00 6.2038023 0.551 0.016 0.000000e+00 7 GZMA
## 69 0.000000e+00 6.1773328 0.764 0.025 0.000000e+00 7 TRAC
## 70 3.158396e-292 6.1292220 0.583 0.022 8.843508e-290 7 CCL5
## 71 4.485128e-230 6.1066864 0.354 0.009 1.255836e-227 7 CD247
## 72 3.283038e-61 6.0487008 0.079 0.001 9.192507e-59 7 GZMK
## 73 9.214896e-60 6.0236889 0.102 0.003 2.580171e-57 7 GNLY
## 74 1.057008e-139 5.8774293 0.244 0.007 2.959622e-137 7 CD69
## 75 9.851276e-104 5.6622324 0.189 0.006 2.758357e-101 7 NKG7
## 76 0.000000e+00 7.7631017 0.800 0.008 0.000000e+00 8 CLEC14A
## 77 0.000000e+00 7.7159366 0.900 0.016 0.000000e+00 8 VWF
## 78 0.000000e+00 6.7682404 0.686 0.012 0.000000e+00 8 KDR
## 79 0.000000e+00 6.7405943 0.629 0.011 0.000000e+00 8 MMRN2
## 80 2.475225e-167 6.7309029 0.257 0.003 6.930631e-165 8 ESM1
## 81 1.157115e-114 6.6070967 0.171 0.002 3.239923e-112 8 SOX18
## 82 2.358859e-121 6.4735951 0.186 0.002 6.604804e-119 8 IL3RA
## 83 1.108004e-198 6.2414925 0.386 0.007 3.102411e-196 8 HOXD9
## 84 1.893055e-291 6.2061499 0.900 0.035 5.300555e-289 8 CD93
## 85 4.691615e-108 6.1590304 0.214 0.004 1.313652e-105 8 NOSTRINCluster annotation: 0 Basal-like epithelial cells / Myoepithelial cells 1 Proliferating epithelial cells 2 Basal/myoepithelial tumor cells 3 Unclassified immune-like cells 4 Inflammatory immune cells (e.g. B cells / monocytes) 5 Cancer-associated fibroblasts (CAFs) 6 Macrophages (TAMs) 7 Cytotoxic / Activated T cells 8 Endothelial cells
table(xenium.obj@meta.data[,cluster_col])##
## 0 1 2 3 4 5 6 7 8
## 1794 1125 1060 906 827 754 610 127 70xenium.obj@meta.data$new_cluster = dplyr::case_when(
xenium.obj@meta.data[,cluster_col] == "0" ~ "BasalMyo_epithelial_cells",
xenium.obj@meta.data[,cluster_col] == "1" ~ "Proliferating_epithelial_cells",
xenium.obj@meta.data[,cluster_col] == "2" ~ "BasalMyo_tumor_cells",
xenium.obj@meta.data[,cluster_col] == "3" ~ "Unclassified_immune-like_cells",
xenium.obj@meta.data[,cluster_col] == "4" ~ "Inflammatory_immune_cells",
xenium.obj@meta.data[,cluster_col] == "5" ~ "Cancer-associated_fibroblasts",
xenium.obj@meta.data[,cluster_col] == "6" ~ "Tumor-associated_macrophages",
xenium.obj@meta.data[,cluster_col] == "7" ~ "Cytotoxic_T",
xenium.obj@meta.data[,cluster_col] == "8" ~ "Endothelial_cells"
)
table(xenium.obj@meta.data$new_cluster)##
## BasalMyo_epithelial_cells BasalMyo_tumor_cells
## 1794 1060
## Cancer-associated_fibroblasts Cytotoxic_T
## 754 127
## Endothelial_cells Inflammatory_immune_cells
## 70 827
## Proliferating_epithelial_cells Tumor-associated_macrophages
## 1125 610
## Unclassified_immune-like_cells
## 906cluster_col = "new_cluster"
Idents(xenium.obj) = xenium.obj@meta.data$new_clustercluster_COLORS = manual_colors
names(cluster_COLORS) = levels(Idents(xenium.obj))
cluster_COLORS = na.omit(cluster_COLORS)
g = CellDimPlot(
srt = xenium.obj,
group.by = cluster_col,
reduction = "UMAP", theme_use = "theme_blank",
raster = FALSE,
stat_plot_size = 1
) &
scale_color_manual(values = cluster_COLORS) &
guides(color = guide_legend(override.aes = list(size=5,
alpha = 1),
title = "cluster",
ncol = 1))
g 
ggsave(paste0("./output/figure/",figure_dir,"/",data_name,"/CellDimPlot.pdf"), g, width=7, height=4, dpi=300)g = ImageDimPlot(xenium.obj,
fov = "fov",
size = 1.2,
group.by = cluster_col,
dark.background = F) &
scale_fill_manual(values = cluster_COLORS) &
guides(fill = guide_legend(override.aes = list(size=5,
alpha = 1),
title = "cluster",
ncol = 1))
g 
ggsave(paste0("./output/figure/",figure_dir,"/",data_name,"/ImageDimPlot_byClusters.pdf"), g, width=6, height=4, dpi=300)
print(xenium.obj)## An object of class Seurat
## 821 features across 7273 samples within 5 assays
## Active assay: SCT (280 features, 280 variable features)
## 3 layers present: counts, data, scale.data
## 4 other assays present: Spatial, BlankCodeword, ControlCodeword, ControlProbe
## 2 dimensional reductions calculated: pca, umap
## 1 spatial field of view present: fov1.2 Spatial neighborhood analysis (SNA, cell type level analysis)
n_perm = 100
neighbors.k_ = 30 # Number of neighbors to search
seed = 1234
start_time = Sys.time()
xenium.obj <- nhood_enrichment(
xenium.obj,
cluster_key = cluster_col,
neighbors.k = neighbors.k_,
connectivity_key = "nn",
transformation = TRUE,
n_perms = n_perm, seed = seed, n_jobs = 4
)
end_time = Sys.time()
print("Elapsed time:")## [1] "Elapsed time:"difftime(end_time, start_time, units = "secs")## Time difference of 10.09798 secsmat = xenium.obj@misc[[paste0(cluster_col,"_nhood_enrichment")]]$zscore
colnames(mat) = gsub("^Cluster","",colnames(mat))
rownames(mat) = gsub("^Cluster","",rownames(mat))
pval_mat <- 1 - pnorm(mat)
# 行列をベクトルにし、一括で多重検定補正
# → 元の行列次元に再度変形
fdr_vec <- p.adjust(as.vector(pval_mat), method = "BH")
fdr_mat <- matrix(fdr_vec, nrow=nrow(mat), ncol=ncol(mat),
dimnames = dimnames(mat))
# --- 3) FDR<0.05 なら `*` を付与 ---
sig_mat <- ifelse(fdr_mat < 0.05, "**", ifelse(fdr_mat > 0.05 & fdr_mat < 0.1, "*", ""))
common_names <- intersect(rownames(mat), colnames(mat))
for (nm in common_names) {
mat[nm, nm] <- NA
sig_mat[nm, nm] <- ""
}
heatmap <- Heatmap(mat,
name = "Z-score",
col = colorRamp2(c(-2, 0, 2), c("#0072B5FF", "white", "#BC3C29FF")),
show_row_names = TRUE,
show_column_names = TRUE,
cluster_rows = TRUE,
cluster_columns = TRUE,
#show_column_dend = FALSE,
#show_row_dend = FALSE,
row_title = "",
column_title = paste0("Spatial Neigborhood Enrichment\n",data_name),
rect_gp = gpar(col = "black", lwd = 0.3),
na_col = "black", # ← NA を黒色で塗りつぶす
column_names_gp = grid::gpar(fontsize = 10),
row_names_gp = grid::gpar(fontsize = 10),
# cell_fun で各セルに注釈を描く
cell_fun = function(j, i, x, y, width, height, fill) {
if(sig_mat[i, j] == "**") {
grid.text("**",
x = x,
y = y - 0.2 * height, # 中心より下にオフセット,
gp = gpar(fontsize = 15, col = "white", fontface = "bold"))
}
if(sig_mat[i, j] == "*") {
grid.text("*",
x = x,
y = y - 0.2 * height, # 中心より下にオフセット,
gp = gpar(fontsize = 15, col = "white", fontface = "bold"))
}
}
)
#pdf(paste0("./output/figure/",figure_dir,"/",data_name,"/SNA_heatmap.pdf"), width=4.5, height=5.5)
draw(heatmap,
merge_legend = TRUE,
heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
#dev.off()1.3 Spatial co-localization score (sCLA, cell-cell level analysis)
cluster_x <- "Cytotoxic_T"
cluster_y <- "Cancer-associated_fibroblasts"
table(Idents(xenium.obj))##
## BasalMyo_tumor_cells BasalMyo_epithelial_cells
## 1060 1794
## Unclassified_immune-like_cells Proliferating_epithelial_cells
## 906 1125
## Tumor-associated_macrophages Cytotoxic_T
## 610 127
## Cancer-associated_fibroblasts Endothelial_cells
## 754 70
## Inflammatory_immune_cells
## 827radius_ = 30 # Radius to search for neighbors (µm, cell_type_2) around anchor cells (cell_type_1)
start_time = Sys.time()
cooccur_local_df <- cooccur_local(
xenium.obj,
cluster_key = cluster_col,
sample_key = NULL,
cluster_x = cluster_x,
cluster_y = cluster_y,
connectivity_key = "nn",
neighbors.k = neighbors.k_,
radius = radius_,
maxnsteps = 15
)
end_time = Sys.time()
print("Elapsed time:")## [1] "Elapsed time:"difftime(end_time, start_time, units = "secs")## Time difference of 1.683462 secssummary(cooccur_local_df)## cooccur_local_Cytotoxic_T_Cancer-associated_fibroblasts
## Min. :0.00000
## 1st Qu.:0.00000
## Median :0.00000
## Mean :0.11811
## 3rd Qu.:0.03125
## Max. :1.13205xenium.obj = AddMetaData(xenium.obj, cooccur_local_df)
g = ImageFeaturePlot(xenium.obj,
features = paste0("cooccur_local_",cluster_x,"_",cluster_y),
#dark.background = F,
cols = c("white", "red"))
g
ggsave(paste0("./output/figure/",figure_dir,"/",data_name,"/ImageFeaturePlot_",cluster_x,"_",cluster_y,".pdf"), g, width=6, height=4, dpi=300)2 Xenium Mouse Brain
- wget https://cf.10xgenomics.com/samples/xenium/1.0.2/Xenium_V1_FF_Mouse_Brain_Coronal_Subset_CTX_HP/Xenium_V1_FF_Mouse_Brain_Coronal_Subset_CTX_HP_outs.zip
- unzip Xenium_V1_FF_Mouse_Brain_Coronal_Subset_CTX_HP_outs.zip
2.1 Preprocessing & Cell annotation
path <- "./../10X_Xenium_sample/Xenium_V1_FF_Mouse_Brain_Coronal_Subset_CTX_HP_outs/"
data_name = stringr::str_split(path, "/")[[1]][length(stringr::str_split(path, "/")[[1]])-1]
dir.create(paste0("./output/figure/",figure_dir,"/",data_name), showWarnings = FALSE)
xenium.obj <- LoadXenium(path, fov = "fov")
# remove cells with 0 counts
xenium.obj <- subset(xenium.obj, subset = nCount_Xenium > 0)
VlnPlot(xenium.obj, features = c("nFeature_Xenium", "nCount_Xenium"), ncol = 2, pt.size = 0)
ImageDimPlot(xenium.obj,
fov = "fov",
size = 0.5,
molecules = c("Gad1", "Sst", "Pvalb", "Gfap"), nmols = 20000)
xenium.obj <- SCTransform(xenium.obj, assay = "Xenium")
xenium.obj <- RunPCA(xenium.obj, npcs = 30, features = rownames(xenium.obj))
xenium.obj <- RunUMAP(xenium.obj, dims = 1:30)
xenium.obj <- FindNeighbors(xenium.obj, reduction = "pca", dims = 1:30)
xenium.obj <- FindClusters(xenium.obj, resolution = 0.3)## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 36553
## Number of edges: 1341724
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9586
## Number of communities: 29
## Elapsed time: 5 secondscluster_col="seurat_clusters"
g = CellDimPlot(
srt = xenium.obj,
group.by = cluster_col,
reduction = "UMAP", theme_use = "theme_blank",
raster = FALSE,
stat_plot_size = 3
)
g 
FeaturePlot(xenium.obj, features = c("Cux2", "Bcl11b", "Foxp2", "Gad1", "Sst", "Gfap"))
ImageDimPlot(xenium.obj, cols = "polychrome",
size = 0.75)
# devtools::install_github("dmcable/spacexr", build_vignettes = FALSE)
library(spacexr)
query.counts <- GetAssayData(xenium.obj, assay = "Xenium", slot = "counts")
coords <- GetTissueCoordinates(xenium.obj[["fov"]], which = "centroids")
rownames(coords) <- coords$cell
coords$cell <- NULL
query <- SpatialRNA(coords, query.counts, colSums(query.counts))# allen.corted.ref can be downloaded here:
# https://www.dropbox.com/s/cuowvm4vrf65pvq/allen_cortex.rds?dl=1
allen.cortex.ref <- readRDS("./../10X_Xenium_sample/allen_cortex.rds")
allen.cortex.ref <- UpdateSeuratObject(allen.cortex.ref)
Idents(allen.cortex.ref) <- "subclass"
# remove CR cells because there aren't enough of them for annotation
allen.cortex.ref <- subset(allen.cortex.ref, subset = subclass != "CR")
counts <- GetAssayData(allen.cortex.ref, assay = "RNA", slot = "counts")
cluster <- as.factor(allen.cortex.ref$subclass)
names(cluster) <- colnames(allen.cortex.ref)
nUMI <- allen.cortex.ref$nCount_RNA
names(nUMI) <- colnames(allen.cortex.ref)
nUMI <- colSums(counts)
levels(cluster) <- gsub("/", "-", levels(cluster))
reference <- Reference(counts, cluster, nUMI)# run RCTD with many cores
RCTD <- create.RCTD(query, reference, max_cores = 8)##
## Astro Endo L2-3 IT L4 L5 IT L5 PT L6 CT
## 368 94 982 1401 880 544 960
## L6 IT L6b Lamp5 Macrophage Meis2 NP Oligo
## 1872 358 1122 51 45 362 91
## Peri Pvalb Serpinf1 SMC Sncg Sst Vip
## 32 1337 27 55 125 1741 1728
## VLMC
## 67RCTD <- run.RCTD(RCTD, doublet_mode = "doublet")## [1] "gather_results: finished 1000"
## [1] "gather_results: finished 2000"
## [1] "gather_results: finished 3000"
## [1] "gather_results: finished 4000"
## [1] "gather_results: finished 5000"
## [1] "gather_results: finished 6000"
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## [1] "gather_results: finished 8000"
## [1] "gather_results: finished 9000"
## [1] "gather_results: finished 10000"
## [1] "gather_results: finished 11000"
## [1] "gather_results: finished 12000"
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## [1] "gather_results: finished 14000"
## [1] "gather_results: finished 15000"
## [1] "gather_results: finished 16000"
## [1] "gather_results: finished 17000"
## [1] "gather_results: finished 18000"
## [1] "gather_results: finished 19000"
## [1] "gather_results: finished 20000"
## [1] "gather_results: finished 21000"
## [1] "gather_results: finished 22000"
## [1] "gather_results: finished 23000"
## [1] "gather_results: finished 24000"
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## [1] "gather_results: finished 26000"
## [1] "gather_results: finished 27000"
## [1] "gather_results: finished 28000"
## [1] "gather_results: finished 29000"
## [1] "gather_results: finished 30000"
## [1] "gather_results: finished 31000"annotations.df <- RCTD@results$results_df
annotations <- annotations.df$first_type
names(annotations) <- rownames(annotations.df)
xenium.obj$predicted.celltype <- annotations
keep.cells <- Cells(xenium.obj)[!is.na(xenium.obj$predicted.celltype)]
xenium.obj <- subset(xenium.obj, cells = keep.cells)cluster_col="predicted.celltype"
Idents(xenium.obj) = xenium.obj@meta.data[,cluster_col]markers = xenium.obj.markers %>%
group_by(cluster) %>%
dplyr::slice_max(avg_log2FC, n = 1) %>%
as.data.frame() %>%
pull(gene) %>%
unique()
marker_COLORS = manual_colors
names(marker_COLORS) = markers
ImageDimPlot(xenium.obj, fov = "fov", molecules = markers, nmols = 20000) &
scale_color_manual(values = marker_COLORS) 
cluster_COLORS = manual_colors
names(cluster_COLORS) = levels(Idents(xenium.obj))
cluster_COLORS = na.omit(cluster_COLORS)
g = CellDimPlot(
srt = xenium.obj,
group.by = cluster_col,
reduction = "UMAP", theme_use = "theme_blank",
raster = FALSE,
stat_plot_size = 1
) &
scale_color_manual(values = cluster_COLORS) &
guides(color = guide_legend(override.aes = list(size=5,
alpha = 1),
title = "cluster",
ncol = 1))
g 
ggsave(paste0("./output/figure/",figure_dir,"/",data_name,"/CellDimPlot.pdf"), g, width=7, height=4, dpi=300)
print(xenium.obj)## An object of class Seurat
## 789 features across 31141 samples within 5 assays
## Active assay: SCT (248 features, 248 variable features)
## 3 layers present: counts, data, scale.data
## 4 other assays present: Xenium, BlankCodeword, ControlCodeword, ControlProbe
## 2 dimensional reductions calculated: pca, umap
## 1 spatial field of view present: fovg = ImageDimPlot(xenium.obj,
fov = "fov",
size = 0.5,
group.by = cluster_col) &
scale_fill_manual(values = cluster_COLORS)
g 
ggsave(paste0("./output/figure/",figure_dir,"/",data_name,"/ImageDimPlot_byClusters.pdf"), g, width=6, height=4, dpi=300)2.2 Spatial neighborhood analysis (SNA, cell type level analysis)
n_perm = 100
neighbors.k_ = 30 # Number of neighbors to search
seed = 1234
start_time = Sys.time()
xenium.obj <- nhood_enrichment(
xenium.obj,
cluster_key = cluster_col,
neighbors.k = neighbors.k_,
connectivity_key = "nn",
transformation = TRUE,
n_perms = n_perm, seed = seed, n_jobs = 4
)
end_time = Sys.time()
print("Elapsed time:")## [1] "Elapsed time:"difftime(end_time, start_time, units = "secs")## Time difference of 36.60824 secsmat = xenium.obj@misc[[paste0(cluster_col,"_nhood_enrichment")]]$zscore
colnames(mat) = gsub("^Cluster","",colnames(mat))
rownames(mat) = gsub("^Cluster","",rownames(mat))
pval_mat <- 1 - pnorm(mat)
# 行列をベクトルにし、一括で多重検定補正
# → 元の行列次元に再度変形
fdr_vec <- p.adjust(as.vector(pval_mat), method = "BH")
fdr_mat <- matrix(fdr_vec, nrow=nrow(mat), ncol=ncol(mat),
dimnames = dimnames(mat))
# --- 3) FDR<0.05 なら `*` を付与 ---
sig_mat <- ifelse(fdr_mat < 0.05, "**", ifelse(fdr_mat > 0.05 & fdr_mat < 0.1, "*", ""))
common_names <- intersect(rownames(mat), colnames(mat))
for (nm in common_names) {
mat[nm, nm] <- NA
sig_mat[nm, nm] <- ""
}
heatmap <- Heatmap(mat,
name = "Z-score",
col = colorRamp2(c(-2, 0, 2), c("#0072B5FF", "white", "#BC3C29FF")),
show_row_names = TRUE,
show_column_names = TRUE,
cluster_rows = TRUE,
cluster_columns = TRUE,
#show_column_dend = FALSE,
#show_row_dend = FALSE,
row_title = "",
column_title = paste0("Spatial Neigborhood Enrichment\n",data_name),
rect_gp = gpar(col = "black", lwd = 0.3),
na_col = "black", # ← NA を黒色で塗りつぶす
column_names_gp = grid::gpar(fontsize = 10),
row_names_gp = grid::gpar(fontsize = 10),
# cell_fun で各セルに注釈を描く
cell_fun = function(j, i, x, y, width, height, fill) {
if(sig_mat[i, j] == "**") {
grid.text("**",
x = x,
y = y - 0.2 * height, # 中心より下にオフセット,
gp = gpar(fontsize = 15, col = "white", fontface = "bold"))
}
if(sig_mat[i, j] == "*") {
grid.text("*",
x = x,
y = y - 0.2 * height, # 中心より下にオフセット,
gp = gpar(fontsize = 15, col = "white", fontface = "bold"))
}
}
)
#pdf(paste0("./output/figure/",figure_dir,"/",data_name,"/SNA_heatmap.pdf"), width=4.5, height=5.5)
draw(heatmap,
merge_legend = TRUE,
heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
#dev.off()2.3 Spatial co-localization score (sCLA, cell-cell level analysis)
cluster_x <- "L6 IT"
cluster_y <- "L6 CT"
table(Idents(xenium.obj))##
## Astro Endo L2-3 IT L4 L5 IT L5 PT L6 CT
## 5468 1773 4563 3679 1863 1896 2182
## L6 IT L6b Lamp5 Macrophage Meis2 NP Oligo
## 1114 355 521 448 82 277 3892
## Peri Pvalb Serpinf1 SMC Sncg Sst Vip
## 239 604 56 24 351 223 114
## VLMC
## 1417radius_ = 30 # Radius to search for neighbors (µm, cell_type_2) around anchor cells (cell_type_1)
start_time = Sys.time()
cooccur_local_df <- cooccur_local(
xenium.obj,
cluster_key = cluster_col,
sample_key = NULL,
cluster_x = cluster_x,
cluster_y = cluster_y,
connectivity_key = "nn",
neighbors.k = neighbors.k_,
radius = radius_,
maxnsteps = 15
)
end_time = Sys.time()
print("Elapsed time:")## [1] "Elapsed time:"difftime(end_time, start_time, units = "secs")## Time difference of 7.286506 secscolnames(cooccur_local_df) = gsub(" ", "_", colnames(cooccur_local_df))
summary(cooccur_local_df)## cooccur_local_L6_IT_L6_CT
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.0929
## 3rd Qu.:0.0000
## Max. :1.0787xenium.obj = AddMetaData(xenium.obj, cooccur_local_df)
g = ImageFeaturePlot(xenium.obj,
features = gsub(" ", "_", paste0("cooccur_local_",cluster_x,"_",cluster_y)),
#dark.background = F,
cols = c("white", "red"))
g
ggsave(paste0("./output/figure/",figure_dir,"/",data_name,"/ImageFeaturePlot_",gsub(" ", "_", cluster_x),"_",gsub(" ", "_", cluster_y),".pdf"), g, width=6, height=4, dpi=300)cluster_COLORS_ = cluster_COLORS[cluster_x]
g_x = ImageDimPlot(xenium.obj,
fov = "fov",
size = 0.3,
group.by = cluster_col) &
scale_fill_manual(values = cluster_COLORS_) &
guides(fill = guide_legend(override.aes = list(size=5,
alpha = 1),
title = "cluster",
ncol = 1))
cluster_COLORS_ = cluster_COLORS[cluster_y]
g_y = ImageDimPlot(xenium.obj,
fov = "fov",
size = 0.3,
group.by = cluster_col) &
scale_fill_manual(values = cluster_COLORS_) &
guides(fill = guide_legend(override.aes = list(size=5,
alpha = 1),
title = "cluster",
ncol = 1))
g_x + g_y
ggsave(paste0("./output/figure/",figure_dir,"/",data_name,"/ImageDimPlot_",gsub(" ", "_", cluster_x),"_",gsub(" ", "_", cluster_y),".pdf"), g_x + g_y, width=6, height=4, dpi=300)3 sessionInfo
sessionInfo()## R version 4.3.2 (2023-10-31)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS 15.3.2
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/Denver
## tzcode source: internal
##
## attached base packages:
## [1] parallel grid stats4 stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] spacexr_2.2.1 msigdbr_7.5.1
## [3] fgsea_1.28.0 randomcoloR_1.1.0.1
## [5] SCP_0.5.6 gridExtra_2.3
## [7] RANN_2.6.1 ComplexHeatmap_2.18.0
## [9] CellChat_2.1.2 bigmemory_4.6.4
## [11] scales_1.3.0 circlize_0.4.15
## [13] ggraph_2.1.0 Matrix_1.6-5
## [15] pheatmap_1.0.12 ggalluvial_0.12.5
## [17] StabMap_0.1.8 igraph_1.6.0
## [19] biomaRt_2.58.2 MOFAdata_1.18.0
## [21] MOFA2_1.13.0 scDblFinder_1.16.0
## [23] SingleCellExperiment_1.24.0 SummarizedExperiment_1.32.0
## [25] Biobase_2.62.0 GenomicRanges_1.54.1
## [27] GenomeInfoDb_1.38.2 IRanges_2.36.0
## [29] S4Vectors_0.40.2 BiocGenerics_0.48.1
## [31] MatrixGenerics_1.14.0 matrixStats_1.2.0
## [33] ggrastr_1.0.2 harmony_1.2.0
## [35] Rcpp_1.0.11 dplyr_1.1.4
## [37] magrittr_2.0.3 scCustomize_2.0.1
## [39] ggpubr_0.6.0 ggsci_3.0.0
## [41] ggrepel_0.9.4 ggplot2_3.4.4
## [43] patchwork_1.1.3 Seurat_5.0.0
## [45] SeuratObject_5.0.2 sp_2.1-2
## [47] BiocStyle_2.30.0
##
## loaded via a namespace (and not attached):
## [1] ica_1.0-3 plotly_4.10.3
## [3] scater_1.30.1 rematch2_2.1.2
## [5] zlibbioc_1.48.0 tidyselect_1.2.0
## [7] bit_4.0.5 doParallel_1.0.17
## [9] clue_0.3-65 lattice_0.21-9
## [11] rjson_0.2.21 blob_1.2.4
## [13] stringr_1.5.1 rngtools_1.5.2
## [15] S4Arrays_1.2.0 png_0.1-8
## [17] cli_3.6.2 ggplotify_0.1.2
## [19] registry_0.5-1 goftest_1.2-3
## [21] textshaping_0.3.7 BiocIO_1.12.0
## [23] glmGamPoi_1.14.0 bluster_1.12.0
## [25] purrr_1.0.2 basilisk.utils_1.14.1
## [27] BiocNeighbors_1.20.1 ggnetwork_0.5.12
## [29] Signac_1.14.0 shadowtext_0.1.2
## [31] uwot_0.1.16 curl_5.2.0
## [33] tidytree_0.4.6 mime_0.12
## [35] evaluate_0.23 leiden_0.4.3.1
## [37] V8_6.0.0 stringi_1.8.3
## [39] backports_1.4.1 slingshot_2.10.0
## [41] XML_3.99-0.16 lubridate_1.9.3
## [43] httpuv_1.6.13 AnnotationDbi_1.64.1
## [45] paletteer_1.5.0 clusterProfiler_4.10.0
## [47] rappdirs_0.3.3 splines_4.3.2
## [49] RcppRoll_0.3.0 corrplot_0.92
## [51] sctransform_0.4.1 ggbeeswarm_0.7.2
## [53] arrow_17.0.0.1 DBI_1.2.0
## [55] HDF5Array_1.30.0 jquerylib_0.1.4
## [57] withr_2.5.2 systemfonts_1.0.5
## [59] enrichplot_1.22.0 xgboost_1.7.6.1
## [61] lmtest_0.9-40 ggnewscale_0.4.9
## [63] tidygraph_1.3.0 rtracklayer_1.62.0
## [65] BiocManager_1.30.22 htmlwidgets_1.6.4
## [67] fs_1.6.3 princurve_2.1.6
## [69] statnet.common_4.9.0 labeling_0.4.3
## [71] SparseArray_1.2.3 reticulate_1.35.0
## [73] zoo_1.8-12 XVector_0.42.0
## [75] knitr_1.45 network_1.18.2
## [77] timechange_0.2.0 foreach_1.5.2
## [79] fansi_1.0.6 data.table_1.16.0
## [81] ggtree_3.10.0 rhdf5_2.46.1
## [83] R.oo_1.25.0 RSpectra_0.16-1
## [85] irlba_2.3.5.1 fastDummies_1.7.3
## [87] gridGraphics_0.5-1 ellipsis_0.3.2
## [89] lazyeval_0.2.2 yaml_2.3.8
## [91] survival_3.5-7 scattermore_1.2
## [93] BiocVersion_3.18.1 crayon_1.5.2
## [95] RcppAnnoy_0.0.21 RColorBrewer_1.1-3
## [97] tidyr_1.3.0 progressr_0.14.0
## [99] tweenr_2.0.2 later_1.3.2
## [101] ggridges_0.5.5 codetools_0.2-19
## [103] GlobalOptions_0.1.2 KEGGREST_1.42.0
## [105] Rtsne_0.17 shape_1.4.6
## [107] limma_3.58.1 Rsamtools_2.18.0
## [109] filelock_1.0.3 pkgconfig_2.0.3
## [111] xml2_1.3.6 spatstat.univar_3.1-2
## [113] GenomicAlignments_1.38.0 aplot_0.2.2
## [115] ape_5.7-1 spatstat.sparse_3.1-0
## [117] viridisLite_0.4.2 gridBase_0.4-7
## [119] xtable_1.8-4 highr_0.10
## [121] car_3.1-2 plyr_1.8.9
## [123] httr_1.4.7 tools_4.3.2
## [125] globals_0.16.2 beeswarm_0.4.0
## [127] broom_1.0.5 nlme_3.1-163
## [129] HDO.db_0.99.1 dbplyr_2.4.0
## [131] assertthat_0.2.1 digest_0.6.33
## [133] bookdown_0.37 dir.expiry_1.10.0
## [135] farver_2.1.1 reshape2_1.4.4
## [137] TrajectoryUtils_1.10.0 yulab.utils_0.1.2
## [139] viridis_0.6.4 glue_1.6.2
## [141] cachem_1.0.8 BiocFileCache_2.10.1
## [143] polyclip_1.10-6 UpSetR_1.4.0
## [145] proxyC_0.3.4 generics_0.1.3
## [147] Biostrings_2.70.1 presto_1.0.0
## [149] parallelly_1.36.0 statmod_1.5.0
## [151] R.cache_0.16.0 ragg_1.2.7
## [153] RcppHNSW_0.5.0 ScaledMatrix_1.10.0
## [155] carData_3.0-5 pbapply_1.7-2
## [157] spam_2.10-0 gson_0.1.0
## [159] dqrng_0.3.2 utf8_1.2.4
## [161] basilisk_1.14.1 graphlayouts_1.0.2
## [163] ggsignif_0.6.4 shiny_1.8.0
## [165] GenomeInfoDbData_1.2.11 R.utils_2.12.3
## [167] rhdf5filters_1.14.1 RCurl_1.98-1.13
## [169] memoise_2.0.1 rmarkdown_2.25
## [171] R.methodsS3_1.8.2 future_1.33.1
## [173] svglite_2.1.3 Cairo_1.6-2
## [175] bigmemory.sri_0.1.8 spatstat.data_3.1-4
## [177] rstudioapi_0.15.0 cluster_2.1.4
## [179] janitor_2.2.0 spatstat.utils_3.1-2
## [181] hms_1.1.3 fitdistrplus_1.1-11
## [183] munsell_0.5.0 cowplot_1.1.2
## [185] colorspace_2.1-0 FNN_1.1.3.2
## [187] quadprog_1.5-8 rlang_1.1.2
## [189] DelayedMatrixStats_1.24.0 sparseMatrixStats_1.14.0
## [191] dotCall64_1.1-1 ggforce_0.4.1
## [193] scuttle_1.12.0 xfun_0.41
## [195] coda_0.19-4 sna_2.7-2
## [197] iterators_1.0.14 abind_1.4-5
## [199] interactiveDisplayBase_1.40.0 GOSemSim_2.28.0
## [201] tibble_3.2.1 treeio_1.26.0
## [203] Rhdf5lib_1.24.1 bitops_1.0-7
## [205] promises_1.2.1 scatterpie_0.2.1
## [207] RSQLite_2.3.4 qvalue_2.34.0
## [209] DelayedArray_0.28.0 GO.db_3.18.0
## [211] compiler_4.3.2 forcats_1.0.0
## [213] prettyunits_1.2.0 beachmat_2.18.0
## [215] listenv_0.9.0 AnnotationHub_3.10.0
## [217] edgeR_4.0.3 BiocSingular_1.18.0
## [219] tensor_1.5 MASS_7.3-60
## [221] progress_1.2.3 uuid_1.1-1
## [223] BiocParallel_1.36.0 babelgene_22.9
## [225] spatstat.random_3.3-2 R6_2.5.1
## [227] fastmap_1.1.1 fastmatch_1.1-4
## [229] rstatix_0.7.2 vipor_0.4.7
## [231] ROCR_1.0-11 rsvd_1.0.5
## [233] gtable_0.3.4 KernSmooth_2.23-22
## [235] miniUI_0.1.1.1 deldir_2.0-2
## [237] htmltools_0.5.7 RcppParallel_5.1.7
## [239] bit64_4.0.5 spatstat.explore_3.3-4
## [241] lifecycle_1.0.4 ggprism_1.0.4
## [243] restfulr_0.0.15 sass_0.4.8
## [245] vctrs_0.6.5 spatstat.geom_3.3-5
## [247] snakecase_0.11.1 DOSE_3.28.2
## [249] scran_1.30.0 NMF_0.26
## [251] ggfun_0.1.3 future.apply_1.11.1
## [253] bslib_0.6.1 pillar_1.9.0
## [255] moments_0.14.1 magick_2.8.2
## [257] metapod_1.10.1 locfit_1.5-9.8
## [259] jsonlite_1.8.8 GetoptLong_1.0.5